Hi, I'm a Dapr CNCF project maintainer. We've recently released Dapr Agents which provides agentic AI features together with built-in durable execution to guarantee statefulness and reliable agentic workflows that run to completion and retry upon failure. It runs natively on Kubernetes, has built-in OTEL integration and uses a lightweight architecture where agents scale to zero, allowing you to run thousands of agents on commodity hardware. It'd be great if you can test it out and give us feedback.
A breakdown of how this compares to other popular agentic AI frameworks (which appear to be multiplying by the day) would be helpful - a decision guide of "is this the right package for me", perhaps
I feel like that at this point, we need an AI agent to compare AI agent frameworks.<p>And benchmarks. Well thought out, structured, non-cherry-picked benchmarks to highlight which framework does well in what area.
This space is crowded and largely undifferentiated. I'd look for MCP support, state management, observability, tests, and a high bus factor to stand out. By these measures, DAPR is not competitive; it would not make my shortlist.<p>Assuming you knew there are lots of alternatives, what led you to create it?